Innovation

Opening — Why this matters now Scientific discovery has always been bottlenecked by one thing: human bandwidth. In scientific machine learning (SciML), where physics meets data-driven modeling, that bottleneck shows up as painstaking trial and error—architectures tuned by hand, loss functions adjusted by intuition, and results validated by weeks of computation. Enter AgenticSciML, a new framework from Brown University that asks a radical question: What if AI could not only run the experiment, but design the method itself? ...

What if we stopped asking language models to “be creative”—and instead let them explore creativity the way humans brainstorm: by remixing ideas, nudging boundaries, and iterating through meaningful variations? That’s exactly what Large Language Models as Innovators proposes: a novel framework that leverages the latent embedding space of ideas—not prompts—to drive controlled, domain-agnostic creativity. Rather than relying on handcrafted rules or complex prompting tricks, the authors show how LLMs can generate original and relevant ideas by interpolating between known concepts, evaluating results, and refining outputs over time. ...

In the symphony of innovation, TRIZ has long served as the structured score guiding engineers toward inventive breakthroughs. But what happens when you give the orchestra to a team of AI agents? Enter TRIZ Agents, a bold exploration of how large language model (LLM) agents—armed with tools, prompts, and persona-based roles—can orchestrate a complete innovation cycle using the TRIZ methodology. Cracking the Code of Creativity TRIZ (Theory of Inventive Problem Solving), derived from the study of thousands of patents, offers a time-tested approach to resolving contradictions in engineering design. It formalizes the innovation process through tools like the 40 Inventive Principles and the Contradiction Matrix. However, its structured elegance demands deep domain expertise—something often scarce outside elite R&D centers. ...